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Neutronic analysis of mixed thorium-uranium fuel bundle for CANDU reactors

2019· article· en· W2914517816 on OpenAlexaboutno aff
Mikołaj Oettingen, Katarzyna Skolik, M. Malicki

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2019
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
FundersAcademic Computer Centre Cyfronet, AGH University of Science and TechnologyInfrastruktura PL-Grid
KeywordsBurnupNuclear engineeringThorium fuel cycleFissile materialNeutron transportMOX fuelEnriched uraniumSpent nuclear fuelNuclear fuelUraniumNuclear transmutationBreeder reactorNeutronNuclear physicsEnvironmental sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

The paper shows neutronic analysis of the CANDU (CANada Deuterium Uranium) nuclear reactor fuel channel with mixed thorium-uranium fuel bundles. The numerical model of the fuel channel was designed using The Monte Carlo Continuous Energy Burn-up Code – MCB developed at the AGH University of Science and Technology, Faculty of Energy and Fuels, Department of Nuclear Energy. The super-computer Prometheus available in the frame of the Pl-Grid Infrastructure at the Academic Computer Centre Cyfronet AGH was used for multi-scale calculations. The fuel bundles are composed of two clusters of fuel rods. The neutronic analysis considers detailed numerical simulation of neutron transport in fully heterogeneous geometry of the fuel channel. Moreover, burnup simulations were performed using Transmutation Trajectory Analysis method implemented in the MCB code. In the analysis we mainly consider time evolutions of neutron multiplication factor, fissile 233U, 235U and 239Pu and fertile 238U and 232Th. The simulations were performed for eight scenarios with various fuel composition.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.176
Teacher spread0.169 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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